Polynomial Kernel Function based Support Vectors for Data Stream Clustering

Support Vector Clustering (SVC) is an important clustering algorithm based on Support Vector Machine (SVM) and kernel methods. SVC algorithm performed better than the other traditional clustering methods, such as a global optimum, treatment of data sets of arbitrary shape, no need for specifying the number of clusters, fewer parameters, and easy treatment of high dimensional data. SV clustering consists of two phases, training based support vector machine and labeling clusters. Training phase allowing for Bounded Support Vectors (BSVs), the existing SVStream algorithm is capable of identifying overlapping clusters.